Magnetic neural Network for predictive maintenance

Introduction

Golana Computing is a new start-up company, spin-off from Spintec-CNRS, exploiting a recent scientific and technological breakthrough in the design and fabrication of bio-mimicking magnetic neurons.

Breakthrough

Studying new magnetization reversal schemes, we have inadvertently discovered that domain wall depinning from geometrical traps imitates in many regards the spiking of biological neurons. Based on this, we designed, fabricated, and tested magnetic neurons able to complete bio-mimicking tasks.

Our magnetic neural network performs speech recognition and speaker identification in real-time, without any prior feature extraction. The audio is simply transformed into spikes by a mechanism inspired by the mammalian ear.

Our Goal

Our goal is to develop a technology and fabricate a prototype that extends this unique ability to other types of analog signals, and apply it for predictive maintenance in the manufacturing industry. The present solutions based on mainstream artificial intelligence (AI) struggle because the problems at hand are too fragmented:

1. The training data is too scarce.
2. The model engineering relies on very specific expert knowledge.

Our Solution

Our solution, frugal in terms of data and resources, is based on a “task-agnostic” generic device that is able to identify unusual patterns in the analog signals.

Our bio-mimicking approach should imitate the ability of human technicians, who assess the state of their machines by the sound. In the long term, our technology could be adapted for a variety of AI applications requiring low energy consumption or full privacy.

EIC Transition Call

The EIC Transition call corresponds exactly to our present needs: to accelerate the development and the market readiness of our technology. Moreover, we address explicitly the requirements for Green Digital Devices.

By working “on the edge”, our device reduces the energy and resources required for data transfer, and by imitating biological neurons, it also reduces the energy required for computation.